
#include "classifier.h"
#include "yolodef.h"
#include "parser.h"
#include <pthread.h>
#include <stdio.h>
#include "image.h"
#include "data.h"
#include "network.h"
#ifdef OPENCV
#include "image_opencv.h"
#endif

#ifdef WIN32
    #include <time.h>
    #include "gettimeofday.h"
#else
    #include <sys/time.h>
#endif

float validate_classifier_single(char* datacfg, char* filename, char* weightfile, network* existing_net, int topk_custom);

float* get_regression_values(char** labels, int n)
{
    float* v = (float*)calloc(n, sizeof(float));
    int i;
    for (i = 0; i < n; ++i)
    {
        char* p = strchr(labels[i], ' ');
        *p = 0;
        v[i] = atof(p + 1);
    }
    return v;
}

void train_classifier(char* datacfg, char* cfgfile, char* weightfile, int* gpus, int ngpus, int clear, int dont_show, int mjpeg_port, int calc_topk)
{
    int i;
    float avg_loss = -1;
    char* base = basecfg(cfgfile);
    printf("%s\n", base);
    printf("%d\n", ngpus);
    network* nets = (network*)calloc(ngpus, sizeof(network));
    srand(time(0));
    int seed = rand();
    for (i = 0; i < ngpus; ++i)
    {
        srand(seed);
#ifdef GPU
        cuda_set_device(gpus[i]);
#endif
        nets[i] = parse_network_cfg(cfgfile);
        if (weightfile)
            load_weights(&nets[i], weightfile);
        if (clear)
            *nets[i].seen = 0;
        nets[i].learning_rate *= ngpus;
    }
    srand(time(0));
    network net = nets[0];
    int imgs = net.batch * net.subdivisions * ngpus;
    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    list* options = read_data_cfg(datacfg);
    char* backup_directory = option_find_str(options, "backup", "/backup/");
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* train_list = option_find_str(options, "train", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    char** labels = get_labels(label_list);
    list* plist = get_paths(train_list);
    char** paths = (char**)list_to_array(plist);
    printf("%d\n", plist->size);
    int train_images_num = plist->size;
    clock_t time;
    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.threads = 32;
    args.hierarchy = net.hierarchy;
    args.min = net.min_crop;
    args.max = net.max_crop;
    args.flip = net.flip;
    args.angle = net.angle;
    args.aspect = net.aspect;
    args.exposure = net.exposure;
    args.saturation = net.saturation;
    args.hue = net.hue;
    args.size = net.w > net.h ? net.w : net.h;
    args.paths = paths;
    args.classes = classes;
    args.n = imgs;
    args.m = train_images_num;
    args.labels = labels;
    args.type = CLASSIFICATION_DATA;
#ifdef OPENCV
    //args.threads = 3;
    mat_cv* img = NULL;
    float max_img_loss = 10;
    int number_of_lines = 100;
    int img_size = 1000;
    img = draw_train_chart(max_img_loss, net.max_batches, number_of_lines, img_size, dont_show);
#endif  //OPENCV
    data train;
    data buffer;
    pthread_t load_thread;
    args.d = &buffer;
    load_thread = load_data(args);
    int iter_save = get_current_batch(net);
    int iter_save_last = get_current_batch(net);
    int iter_topk = get_current_batch(net);
    float topk = 0;
    while (get_current_batch(net) < net.max_batches || net.max_batches == 0)
    {
        time = clock();
        pthread_join(load_thread, 0);
        train = buffer;
        load_thread = load_data(args);
        printf("Loaded: %lf seconds\n", sec(clock() - time));
        time = clock();
        float loss = 0;
#ifdef GPU
        if (ngpus == 1)
            loss = train_network(net, train);
        else
            loss = train_networks(nets, ngpus, train, 4);
#else
        loss = train_network(net, train);
#endif
        if (avg_loss == -1)
            avg_loss = loss;
        avg_loss = avg_loss * .9 + loss * .1;
        i = get_current_batch(net);
        int calc_topk_for_each = iter_topk + 2 * train_images_num / (net.batch * net.subdivisions);  // calculate TOPk for each 2 Epochs
        calc_topk_for_each = fmax(calc_topk_for_each, net.burn_in);
        calc_topk_for_each = fmax(calc_topk_for_each, 1000);
        if (i % 10 == 0)
        {
            if (calc_topk)
            {
                fprintf(stderr, "\n (next TOP5 calculation at %d iterations) ", calc_topk_for_each);
                if (topk > 0)
                    fprintf(stderr, " Last accuracy TOP5 = %2.2f %% \n", topk * 100);
            }
            if (net.cudnn_half)
            {
                if (i < net.burn_in * 3)
                    fprintf(stderr, " Tensor Cores are disabled until the first %d iterations are reached.\n", 3 * net.burn_in);
                else
                    fprintf(stderr, " Tensor Cores are used.\n");
            }
        }
        int draw_precision = 0;
        if (calc_topk && (i >= calc_topk_for_each || i == net.max_batches))
        {
            iter_topk = i;
            topk = validate_classifier_single(datacfg, cfgfile, weightfile, &net, 5); // calc TOP5
            printf("\n accuracy TOP5 = %f \n", topk);
            draw_precision = 1;
        }
        printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %ld images\n", get_current_batch(net), (float)(*net.seen) / train_images_num, loss, avg_loss, get_current_rate(net), sec(clock() - time), *net.seen);
#ifdef OPENCV
        draw_train_loss(img, img_size, avg_loss, max_img_loss, i, net.max_batches, topk, draw_precision, "top5", dont_show, mjpeg_port);
#endif  // OPENCV
        if (i >= (iter_save + 1000))
        {
            iter_save = i;
#ifdef GPU
            if (ngpus != 1)
                sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s_%d.mo", backup_directory, base, i);
            save_weights(net, buff);
        }
        if (i >= (iter_save_last + 100))
        {
            iter_save_last = i;
#ifdef GPU
            if (ngpus != 1)
                sync_nets(nets, ngpus, 0);
#endif
            char buff[256];
            sprintf(buff, "%s/%s_last.mo", backup_directory, base);
            save_weights(net, buff);
        }
        free_data(train);
    }
#ifdef GPU
    if (ngpus != 1)
        sync_nets(nets, ngpus, 0);
#endif
    char buff[256];
    sprintf(buff, "%s/%s_final.mo", backup_directory, base);
    save_weights(net, buff);
#ifdef OPENCV
    release_mat(&img);
    destroy_all_windows_cv();
#endif
    free_network(net);
    free_ptrs((void**)labels, classes);
    free_ptrs((void**)paths, plist->size);
    free_list(plist);
    free(base);
}


/*
    void train_classifier(char *datacfg, char *cfgfile, char *weightfile, int clear)
    {
    srand(time(0));
    float avg_loss = -1;
    char *base = basecfg(cfgfile);
    printf("%s\n", base);
    network net = parse_network_cfg(cfgfile);
    if(weightfile){
    load_weights(&net, weightfile);
    }
    if(clear) *net.seen = 0;

    int imgs = net.batch * net.subdivisions;

    printf("Learning Rate: %g, Momentum: %g, Decay: %g\n", net.learning_rate, net.momentum, net.decay);
    list *options = read_data_cfg(datacfg);

    char *backup_directory = option_find_str(options, "backup", "/backup/");
    char *label_list = option_find_str(options, "labels", "data/labels.list");
    char *train_list = option_find_str(options, "train", "data/train.list");
    int classes = option_find_int(options, "classes", 2);

    char **labels = get_labels(label_list);
    list *plist = get_paths(train_list);
    char **paths = (char **)list_to_array(plist);
    printf("%d\n", plist->size);
    int N = plist->size;
    clock_t time;

    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.threads = 8;

    args.min = net.min_crop;
    args.max = net.max_crop;
    args.flip = net.flip;
    args.angle = net.angle;
    args.aspect = net.aspect;
    args.exposure = net.exposure;
    args.saturation = net.saturation;
    args.hue = net.hue;
    args.size = net.w;
    args.hierarchy = net.hierarchy;

    args.paths = paths;
    args.classes = classes;
    args.n = imgs;
    args.m = N;
    args.labels = labels;
    args.type = CLASSIFICATION_DATA;

    data train;
    data buffer;
    pthread_t load_thread;
    args.d = &buffer;
    load_thread = load_data(args);

    int epoch = (*net.seen)/N;
    while(get_current_batch(net) < net.max_batches || net.max_batches == 0){
    time=clock();

    pthread_join(load_thread, 0);
    train = buffer;
    load_thread = load_data(args);

    printf("Loaded: %lf seconds\n", sec(clock()-time));
    time=clock();

    #ifdef OPENCV
    if(0){
    int u;
    for(u = 0; u < imgs; ++u){
    image im = float_to_image(net.w, net.h, 3, train.X.vals[u]);
    show_image(im, "loaded");
    cvWaitKey(0);
    }
    }
    #endif

    float loss = train_network(net, train);
    free_data(train);

    if(avg_loss == -1) avg_loss = loss;
    avg_loss = avg_loss*.9 + loss*.1;
    printf("%d, %.3f: %f, %f avg, %f rate, %lf seconds, %d images\n", get_current_batch(net), (float)(*net.seen)/N, loss, avg_loss, get_current_rate(net), sec(clock()-time), *net.seen);
    if(*net.seen/N > epoch){
    epoch = *net.seen/N;
    char buff[256];
    sprintf(buff, "%s/%s_%d.mo",backup_directory,base, epoch);
    save_weights(net, buff);
    }
    if(get_current_batch(net)%100 == 0){
    char buff[256];
    sprintf(buff, "%s/%s.backup",backup_directory,base);
    save_weights(net, buff);
    }
    }
    char buff[256];
    sprintf(buff, "%s/%s.mo", backup_directory, base);
    save_weights(net, buff);

    free_network(net);
    free_ptrs((void**)labels, classes);
    free_ptrs((void**)paths, plist->size);
    free_list(plist);
    free(base);
    }
*/

void validate_classifier_crop(char* datacfg, char* filename, char* weightfile)
{
    int i = 0;
    network net = parse_network_cfg(filename);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);
    if (topk > classes)
        topk = classes;
    char** labels = get_labels(label_list);
    list* plist = get_paths(valid_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    clock_t time;
    float avg_acc = 0;
    float avg_topk = 0;
    int splits = m / 1000;
    int num = (i + 1) * m / splits - i * m / splits;
    data val, buffer;
    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.paths = paths;
    args.classes = classes;
    args.n = num;
    args.m = 0;
    args.labels = labels;
    args.d = &buffer;
    args.type = OLD_CLASSIFICATION_DATA;
    pthread_t load_thread = load_data_in_thread(args);
    for (i = 1; i <= splits; ++i)
    {
        time = clock();
        pthread_join(load_thread, 0);
        val = buffer;
        num = (i + 1) * m / splits - i * m / splits;
        char** part = paths + (i * m / splits);
        if (i != splits)
        {
            args.paths = part;
            load_thread = load_data_in_thread(args);
        }
        printf("Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock() - time));
        time = clock();
        float* acc = network_accuracies(net, val, topk);
        avg_acc += acc[0];
        avg_topk += acc[1];
        printf("%d: top 1: %f, top %d: %f, %lf seconds, %d images\n", i, avg_acc / i, topk, avg_topk / i, sec(clock() - time), val.X.rows);
        free_data(val);
    }
}

void validate_classifier_10(char* datacfg, char* filename, char* weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);
    if (topk > classes)
        topk = classes;
    char** labels = get_labels(label_list);
    list* plist = get_paths(valid_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    float avg_acc = 0;
    float avg_topk = 0;
    int* indexes = (int*)calloc(topk, sizeof(int));
    for (i = 0; i < m; ++i)
    {
        int class_id = -1;
        char* path = paths[i];
        for (j = 0; j < classes; ++j)
        {
            if (strstr(path, labels[j]))
            {
                class_id = j;
                break;
            }
        }
        int w = net.w;
        int h = net.h;
        int shift = 32;
        image im = load_image_color(paths[i], w + shift, h + shift);
        image images[10];
        images[0] = crop_image(im, -shift, -shift, w, h);
        images[1] = crop_image(im, shift, -shift, w, h);
        images[2] = crop_image(im, 0, 0, w, h);
        images[3] = crop_image(im, -shift, shift, w, h);
        images[4] = crop_image(im, shift, shift, w, h);
        flip_image(im);
        images[5] = crop_image(im, -shift, -shift, w, h);
        images[6] = crop_image(im, shift, -shift, w, h);
        images[7] = crop_image(im, 0, 0, w, h);
        images[8] = crop_image(im, -shift, shift, w, h);
        images[9] = crop_image(im, shift, shift, w, h);
        float* pred = (float*)calloc(classes, sizeof(float));
        for (j = 0; j < 10; ++j)
        {
            float* p = network_predict(net, images[j].data);
            if (net.hierarchy)
                hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
            axpy_cpu(classes, 1, p, 1, pred, 1);
            free_image(images[j]);
        }
        free_image(im);
        top_k(pred, classes, topk, indexes);
        free(pred);
        if (indexes[0] == class_id)
            avg_acc += 1;
        for (j = 0; j < topk; ++j)
        {
            if (indexes[j] == class_id)
                avg_topk += 1;
        }
        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc / (i + 1), topk, avg_topk / (i + 1));
    }
}

void validate_classifier_full(char* datacfg, char* filename, char* weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);
    if (topk > classes)
        topk = classes;
    char** labels = get_labels(label_list);
    list* plist = get_paths(valid_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    float avg_acc = 0;
    float avg_topk = 0;
    int* indexes = (int*)calloc(topk, sizeof(int));
    int size = net.w;
    for (i = 0; i < m; ++i)
    {
        int class_id = -1;
        char* path = paths[i];
        for (j = 0; j < classes; ++j)
        {
            if (strstr(path, labels[j]))
            {
                class_id = j;
                break;
            }
        }
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, size);
        resize_network(&net, resized.w, resized.h);
        //show_image(im, "orig");
        //show_image(crop, "cropped");
        //cvWaitKey(0);
        float* pred = network_predict(net, resized.data);
        if (net.hierarchy)
            hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
        free_image(im);
        free_image(resized);
        top_k(pred, classes, topk, indexes);
        if (indexes[0] == class_id)
            avg_acc += 1;
        for (j = 0; j < topk; ++j)
        {
            if (indexes[j] == class_id)
                avg_topk += 1;
        }
        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc / (i + 1), topk, avg_topk / (i + 1));
    }
}


float validate_classifier_single(char* datacfg, char* filename, char* weightfile, network* existing_net, int topk_custom)
{
    int i, j;
    network net;
    int old_batch = -1;
    if (existing_net)
    {
        net = *existing_net;    // for validation during training
        old_batch = net.batch;
        set_batch_network(&net, 1);
    }
    else
    {
        net = parse_network_cfg_custom(filename, 1, 0);
        if (weightfile)
            load_weights(&net, weightfile);
        //set_batch_network(&net, 1);
        fuse_conv_batchnorm(net);
        calculate_binary_weights(net);
    }
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* leaf_list = option_find_str(options, "leaves", 0);
    if (leaf_list)
        change_leaves(net.hierarchy, leaf_list);
    char* valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);
    if (topk_custom > 0)
        topk = topk_custom;    // for validation during training
    if (topk > classes)
        topk = classes;
    printf(" TOP calculation...\n");
    char** labels = get_labels(label_list);
    list* plist = get_paths(valid_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    float avg_acc = 0;
    float avg_topk = 0;
    int* indexes = (int*)calloc(topk, sizeof(int));
    for (i = 0; i < m; ++i)
    {
        int class_id = -1;
        char* path = paths[i];
        for (j = 0; j < classes; ++j)
        {
            if (strstr(path, labels[j]))
            {
                class_id = j;
                break;
            }
        }
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, net.w);
        image crop = crop_image(resized, (resized.w - net.w) / 2, (resized.h - net.h) / 2, net.w, net.h);
        //show_image(im, "orig");
        //show_image(crop, "cropped");
        //cvWaitKey(0);
        float* pred = network_predict(net, crop.data);
        if (net.hierarchy)
            hierarchy_predictions(pred, net.outputs, net.hierarchy, 1);
        if (resized.data != im.data)
            free_image(resized);
        free_image(im);
        free_image(crop);
        top_k(pred, classes, topk, indexes);
        if (indexes[0] == class_id)
            avg_acc += 1;
        for (j = 0; j < topk; ++j)
        {
            if (indexes[j] == class_id)
                avg_topk += 1;
        }
        if (existing_net)
            printf("\r");
        else
            printf("\n");
        printf("%d: top 1: %f, top %d: %f", i, avg_acc / (i + 1), topk, avg_topk / (i + 1));
    }
    if (existing_net)
        set_batch_network(&net, old_batch);
    float topk_result = avg_topk / i;
    return topk_result;
}

void validate_classifier_multi(char* datacfg, char* filename, char* weightfile)
{
    int i, j;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "labels", "data/labels.list");
    char* valid_list = option_find_str(options, "valid", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    int topk = option_find_int(options, "top", 1);
    if (topk > classes)
        topk = classes;
    char** labels = get_labels(label_list);
    list* plist = get_paths(valid_list);
    int scales[] = {224, 288, 320, 352, 384};
    int nscales = sizeof(scales) / sizeof(scales[0]);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    float avg_acc = 0;
    float avg_topk = 0;
    int* indexes = (int*)calloc(topk, sizeof(int));
    for (i = 0; i < m; ++i)
    {
        int class_id = -1;
        char* path = paths[i];
        for (j = 0; j < classes; ++j)
        {
            if (strstr(path, labels[j]))
            {
                class_id = j;
                break;
            }
        }
        float* pred = (float*)calloc(classes, sizeof(float));
        image im = load_image_color(paths[i], 0, 0);
        for (j = 0; j < nscales; ++j)
        {
            image r = resize_min(im, scales[j]);
            resize_network(&net, r.w, r.h);
            float* p = network_predict(net, r.data);
            if (net.hierarchy)
                hierarchy_predictions(p, net.outputs, net.hierarchy, 1);
            axpy_cpu(classes, 1, p, 1, pred, 1);
            flip_image(r);
            p = network_predict(net, r.data);
            axpy_cpu(classes, 1, p, 1, pred, 1);
            if (r.data != im.data)
                free_image(r);
        }
        free_image(im);
        top_k(pred, classes, topk, indexes);
        free(pred);
        if (indexes[0] == class_id)
            avg_acc += 1;
        for (j = 0; j < topk; ++j)
        {
            if (indexes[j] == class_id)
                avg_topk += 1;
        }
        printf("%d: top 1: %f, top %d: %f\n", i, avg_acc / (i + 1), topk, avg_topk / (i + 1));
    }
}

void try_classifier(char* datacfg, char* cfgfile, char* weightfile, char* filename, int layer_num)
{
    network net = parse_network_cfg_custom(cfgfile, 1, 0);
    if (weightfile)
        load_weights(&net, weightfile);
    set_batch_network(&net, 1);
    srand(2222222);
    list* options = read_data_cfg(datacfg);
    char* name_list = option_find_str(options, "names", 0);
    if (!name_list)
        name_list = option_find_str(options, "labels", "data/labels.list");
    int classes = option_find_int(options, "classes", 2);
    int top = option_find_int(options, "top", 1);
    if (top > classes)
        top = classes;
    int i = 0;
    char** names = get_labels(name_list);
    clock_t time;
    int* indexes = (int*)calloc(top, sizeof(int));
    char buff[256];
    char* input = buff;
    while (1)
    {
        if (filename)
            strncpy(input, filename, 256);
        else
        {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if (!input)
                return;
            strtok(input, "\n");
        }
        image orig = load_image_color(input, 0, 0);
        image r = resize_min(orig, 256);
        image im = crop_image(r, (r.w - 224 - 1) / 2 + 1, (r.h - 224 - 1) / 2 + 1, 224, 224);
        float mean[] = {0.48263312050943, 0.45230225481413, 0.40099074308742};
        float std[] = {0.22590347483426, 0.22120921437787, 0.22103996251583};
        float var[3];
        var[0] = std[0] * std[0];
        var[1] = std[1] * std[1];
        var[2] = std[2] * std[2];
        normalize_cpu(im.data, mean, var, 1, 3, im.w * im.h);
        float* X = im.data;
        time = clock();
        float* predictions = network_predict(net, X);
        layer l = net.layers[layer_num];
        for (i = 0; i < l.c; ++i)
        {
            if (l.rolling_mean)
                printf("%f %f %f\n", l.rolling_mean[i], l.rolling_variance[i], l.scales[i]);
        }
#ifdef GPU
        cuda_pull_array(l.output_gpu, l.output, l.outputs);
#endif
        for (i = 0; i < l.outputs; ++i)
            printf("%f\n", l.output[i]);
        /*

            printf("\n\nWeights\n");
            for(i = 0; i < l.n*l.size*l.size*l.c; ++i){
            printf("%f\n", l.filters[i]);
            }

            printf("\n\nBiases\n");
            for(i = 0; i < l.n; ++i){
            printf("%f\n", l.biases[i]);
            }
        */
        top_predictions(net, top, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock() - time));
        for (i = 0; i < top; ++i)
        {
            int index = indexes[i];
            printf("%s: %f\n", names[index], predictions[index]);
        }
        free_image(im);
        if (filename)
            break;
    }
}

void predict_classifier(char* datacfg, char* cfgfile, char* weightfile, char* filename, int top)
{
    network net = parse_network_cfg_custom(cfgfile, 1, 0);
    if (weightfile)
        load_weights(&net, weightfile);
    set_batch_network(&net, 1);
    srand(2222222);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    list* options = read_data_cfg(datacfg);
    char* name_list = option_find_str(options, "names", 0);
    if (!name_list)
        name_list = option_find_str(options, "labels", "data/labels.list");
    int classes = option_find_int(options, "classes", 2);
    if (top == 0)
        top = option_find_int(options, "top", 1);
    if (top > classes)
        top = classes;
    int i = 0;
    char** names = get_labels(name_list);
    clock_t time;
    int* indexes = (int*)calloc(top, sizeof(int));
    char buff[256];
    char* input = buff;
    //int size = net.w;
    while (1)
    {
        if (filename)
            strncpy(input, filename, 256);
        else
        {
            printf("Enter Image Path: ");
            fflush(stdout);
            input = fgets(input, 256, stdin);
            if (!input)
                return;
            strtok(input, "\n");
        }
        image im = load_image_color(input, 0, 0);
        image r = letterbox_image(im, net.w, net.h);
        //image r = resize_min(im, size);
        //resize_network(&net, r.w, r.h);
        printf("%d %d\n", r.w, r.h);
        float* X = r.data;
        time = clock();
        float* predictions = network_predict(net, X);
        if (net.hierarchy)
            hierarchy_predictions(predictions, net.outputs, net.hierarchy, 0);
        top_k(predictions, net.outputs, top, indexes);
        printf("%s: Predicted in %f seconds.\n", input, sec(clock() - time));
        for (i = 0; i < top; ++i)
        {
            int index = indexes[i];
            if (net.hierarchy)
                printf("%d, %s: %f, parent: %s \n", index, names[index], predictions[index], (net.hierarchy->parent[index] >= 0) ? names[net.hierarchy->parent[index]] : "Root");
            else
                printf("%s: %f\n", names[index], predictions[index]);
        }
        if (r.data != im.data)
            free_image(r);
        free_image(im);
        if (filename)
            break;
    }
}


void label_classifier(char* datacfg, char* filename, char* weightfile)
{
    int i;
    network net = parse_network_cfg(filename);
    set_batch_network(&net, 1);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    list* options = read_data_cfg(datacfg);
    char* label_list = option_find_str(options, "names", "data/labels.list");
    char* test_list = option_find_str(options, "test", "data/train.list");
    int classes = option_find_int(options, "classes", 2);
    char** labels = get_labels(label_list);
    list* plist = get_paths(test_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    for (i = 0; i < m; ++i)
    {
        image im = load_image_color(paths[i], 0, 0);
        image resized = resize_min(im, net.w);
        image crop = crop_image(resized, (resized.w - net.w) / 2, (resized.h - net.h) / 2, net.w, net.h);
        float* pred = network_predict(net, crop.data);
        if (resized.data != im.data)
            free_image(resized);
        free_image(im);
        free_image(crop);
        int ind = max_index(pred, classes);
        printf("%s\n", labels[ind]);
    }
}


void test_classifier(char* datacfg, char* cfgfile, char* weightfile, int target_layer)
{
    int curr = 0;
    network net = parse_network_cfg(cfgfile);
    if (weightfile)
        load_weights(&net, weightfile);
    srand(time(0));
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    list* options = read_data_cfg(datacfg);
    char* test_list = option_find_str(options, "test", "data/test.list");
    int classes = option_find_int(options, "classes", 2);
    list* plist = get_paths(test_list);
    char** paths = (char**)list_to_array(plist);
    int m = plist->size;
    free_list(plist);
    clock_t time;
    data val, buffer;
    load_args args = {0};
    args.w = net.w;
    args.h = net.h;
    args.paths = paths;
    args.classes = classes;
    args.n = net.batch;
    args.m = 0;
    args.labels = 0;
    args.d = &buffer;
    args.type = OLD_CLASSIFICATION_DATA;
    pthread_t load_thread = load_data_in_thread(args);
    for (curr = net.batch; curr < m; curr += net.batch)
    {
        time = clock();
        pthread_join(load_thread, 0);
        val = buffer;
        if (curr < m)
        {
            args.paths = paths + curr;
            if (curr + net.batch > m)
                args.n = m - curr;
            load_thread = load_data_in_thread(args);
        }
        fprintf(stderr, "Loaded: %d images in %lf seconds\n", val.X.rows, sec(clock() - time));
        time = clock();
        matrix pred = network_predict_data(net, val);
        int i, j;
        if (target_layer >= 0)
        {
            //layer l = net.layers[target_layer];
        }
        for (i = 0; i < pred.rows; ++i)
        {
            printf("%s", paths[curr - net.batch + i]);
            for (j = 0; j < pred.cols; ++j)
                printf("\t%g", pred.vals[i][j]);
            printf("\n");
        }
        free_matrix(pred);
        fprintf(stderr, "%lf seconds, %d images, %d total\n", sec(clock() - time), val.X.rows, curr);
        free_data(val);
    }
}


void threat_classifier(char* datacfg, char* cfgfile, char* weightfile, int cam_index, const char* filename)
{
#ifdef OPENCV
    float threat = 0;
    float roll = .2;
    printf("Classifier Demo\n");
    network net = parse_network_cfg(cfgfile);
    if (weightfile)
        load_weights(&net, weightfile);
    set_batch_network(&net, 1);
    list* options = read_data_cfg(datacfg);
    srand(2222222);
    cap_cv* cap;
    if (filename)
    {
        //cap = cvCaptureFromFile(filename);
        cap = get_capture_video_stream(filename);
    }
    else
    {
        //cap = cvCaptureFromCAM(cam_index);
        cap = get_capture_webcam(cam_index);
    }
    int classes = option_find_int(options, "classes", 2);
    int top = option_find_int(options, "top", 1);
    if (top > classes)
        top = classes;
    char* name_list = option_find_str(options, "names", 0);
    char** names = get_labels(name_list);
    int* indexes = (int*)calloc(top, sizeof(int));
    if (!cap)
        error("Couldn't connect to webcam.\n");
    create_window_cv("Threat", 0, 512, 512);
    float fps = 0;
    int i;
    int count = 0;
    while (1)
    {
        ++count;
        struct timeval tval_before, tval_after, tval_result;
        gettimeofday2(&tval_before, NULL);
        //image in = get_image_from_stream(cap);
        image in = get_image_from_stream_cpp(cap);
        if (!in.data)
            break;
        image in_s = resize_image(in, net.w, net.h);
        image out = in;
        int x1 = out.w / 20;
        int y1 = out.h / 20;
        int x2 = 2 * x1;
        int y2 = out.h - out.h / 20;
        int border = .01 * out.h;
        int h = y2 - y1 - 2 * border;
        int w = x2 - x1 - 2 * border;
        float* predictions = network_predict(net, in_s.data);
        float curr_threat = 0;
        if (1)
        {
            curr_threat = predictions[0] * 0 +
                          predictions[1] * .6 +
                          predictions[2];
        }
        else
        {
            curr_threat = predictions[218] +
                          predictions[539] +
                          predictions[540] +
                          predictions[368] +
                          predictions[369] +
                          predictions[370];
        }
        threat = roll * curr_threat + (1 - roll) * threat;
        draw_box_width(out, x2 + border, y1 + .02 * h, x2 + .5 * w, y1 + .02 * h + border, border, 0, 0, 0);
        if (threat > .97)
        {
            draw_box_width(out,  x2 + .5 * w + border,
                           y1 + .02 * h - 2 * border,
                           x2 + .5 * w + 6 * border,
                           y1 + .02 * h + 3 * border, 3 * border, 1, 0, 0);
        }
        draw_box_width(out,  x2 + .5 * w + border,
                       y1 + .02 * h - 2 * border,
                       x2 + .5 * w + 6 * border,
                       y1 + .02 * h + 3 * border, .5 * border, 0, 0, 0);
        draw_box_width(out, x2 + border, y1 + .42 * h, x2 + .5 * w, y1 + .42 * h + border, border, 0, 0, 0);
        if (threat > .57)
        {
            draw_box_width(out,  x2 + .5 * w + border,
                           y1 + .42 * h - 2 * border,
                           x2 + .5 * w + 6 * border,
                           y1 + .42 * h + 3 * border, 3 * border, 1, 1, 0);
        }
        draw_box_width(out,  x2 + .5 * w + border,
                       y1 + .42 * h - 2 * border,
                       x2 + .5 * w + 6 * border,
                       y1 + .42 * h + 3 * border, .5 * border, 0, 0, 0);
        draw_box_width(out, x1, y1, x2, y2, border, 0, 0, 0);
        for (i = 0; i < threat * h ; ++i)
        {
            float ratio = (float) i / h;
            float r = (ratio < .5) ? (2 * (ratio)) : 1;
            float g = (ratio < .5) ? 1 : 1 - 2 * (ratio - .5);
            draw_box_width(out, x1 + border, y2 - border - i, x2 - border, y2 - border - i, 1, r, g, 0);
        }
        top_predictions(net, top, indexes);
        char buff[256];
        sprintf(buff, "tmp/threat_%06d", count);
        //save_image(out, buff);
#ifndef _WIN32
        printf("\033[2J");
        printf("\033[1;1H");
#endif
        printf("\nFPS:%.0f\n", fps);
        for (i = 0; i < top; ++i)
        {
            int index = indexes[i];
            printf("%.1f%%: %s\n", predictions[index] * 100, names[index]);
        }
        if (1)
        {
            show_image(out, "Threat");
            wait_key_cv(10);
        }
        free_image(in_s);
        free_image(in);
        gettimeofday2(&tval_after, NULL);
        timersub(&tval_after, &tval_before, &tval_result);
        float curr = 1000000.f / ((long int)tval_result.tv_usec);
        fps = .9 * fps + .1 * curr;
    }
#endif
}


void gun_classifier(char* datacfg, char* cfgfile, char* weightfile, int cam_index, const char* filename)
{
#ifdef OPENCV_DISABLE
    int bad_cats[] = {218, 539, 540, 1213, 1501, 1742, 1911, 2415, 4348, 19223, 368, 369, 370, 1133, 1200, 1306, 2122, 2301, 2537, 2823, 3179, 3596, 3639, 4489, 5107, 5140, 5289, 6240, 6631, 6762, 7048, 7171, 7969, 7984, 7989, 8824, 8927, 9915, 10270, 10448, 13401, 15205, 18358, 18894, 18895, 19249, 19697};
    printf("Classifier Demo\n");
    network net = parse_network_cfg(cfgfile);
    if (weightfile)
        load_weights(&net, weightfile);
    set_batch_network(&net, 1);
    list* options = read_data_cfg(datacfg);
    srand(2222222);
    CvCapture* cap;
    if (filename)
    {
        //cap = cvCaptureFromFile(filename);
        cap = get_capture_video_stream(filename);
    }
    else
    {
        //cap = cvCaptureFromCAM(cam_index);
        cap = get_capture_webcam(cam_index);
    }
    int classes = option_find_int(options, "classes", 2);
    int top = option_find_int(options, "top", 1);
    if (top > classes)
        top = classes;
    char* name_list = option_find_str(options, "names", 0);
    char** names = get_labels(name_list);
    int* indexes = (int*)calloc(top, sizeof(int));
    if (!cap)
        error("Couldn't connect to webcam.\n");
    cvNamedWindow("Threat Detection", CV_WINDOW_NORMAL);
    cvResizeWindow("Threat Detection", 512, 512);
    float fps = 0;
    int i;
    while (1)
    {
        struct timeval tval_before, tval_after, tval_result;
        gettimeofday2(&tval_before, NULL);
        //image in = get_image_from_stream(cap);
        image in = get_image_from_stream_cpp(cap);
        image in_s = resize_image(in, net.w, net.h);
        show_image(in, "Threat Detection");
        float* predictions = network_predict(net, in_s.data);
        top_predictions(net, top, indexes);
        printf("\033[2J");
        printf("\033[1;1H");
        int threat = 0;
        for (i = 0; i < sizeof(bad_cats) / sizeof(bad_cats[0]); ++i)
        {
            int index = bad_cats[i];
            if (predictions[index] > .01)
            {
                printf("Threat Detected!\n");
                threat = 1;
                break;
            }
        }
        if (!threat)
            printf("Scanning...\n");
        for (i = 0; i < sizeof(bad_cats) / sizeof(bad_cats[0]); ++i)
        {
            int index = bad_cats[i];
            if (predictions[index] > .01)
                printf("%s\n", names[index]);
        }
        free_image(in_s);
        free_image(in);
        cvWaitKey(10);
        gettimeofday2(&tval_after, NULL);
        timersub(&tval_after, &tval_before, &tval_result);
        float curr = 1000000.f / ((long int)tval_result.tv_usec);
        fps = .9 * fps + .1 * curr;
    }
#endif
}

void demo_classifier(char* datacfg, char* cfgfile, char* weightfile, int cam_index, const char* filename)
{
#ifdef OPENCV
    printf("Classifier Demo\n");
    network net = parse_network_cfg_custom(cfgfile, 1, 0);
    if (weightfile)
        load_weights(&net, weightfile);
    set_batch_network(&net, 1);
    list* options = read_data_cfg(datacfg);
    fuse_conv_batchnorm(net);
    calculate_binary_weights(net);
    srand(2222222);
    cap_cv* cap;
    if (filename)
        cap = get_capture_video_stream(filename);
    else
        cap = get_capture_webcam(cam_index);
    int classes = option_find_int(options, "classes", 2);
    int top = option_find_int(options, "top", 1);
    if (top > classes)
        top = classes;
    char* name_list = option_find_str(options, "names", 0);
    char** names = get_labels(name_list);
    int* indexes = (int*)calloc(top, sizeof(int));
    if (!cap)
        error("Couldn't connect to webcam.\n");
    create_window_cv("Classifier", 0, 512, 512);
    float fps = 0;
    int i;
    while (1)
    {
        struct timeval tval_before, tval_after, tval_result;
        gettimeofday2(&tval_before, NULL);
        //image in = get_image_from_stream(cap);
        image in = get_image_from_stream_cpp(cap);
        image in_s = resize_image(in, net.w, net.h);
        show_image(in, "Classifier");
        float* predictions = network_predict(net, in_s.data);
        if (net.hierarchy)
            hierarchy_predictions(predictions, net.outputs, net.hierarchy, 1);
        top_predictions(net, top, indexes);
#ifndef _WIN32
        printf("\033[2J");
        printf("\033[1;1H");
#endif
        printf("\nFPS:%.0f\n", fps);
        for (i = 0; i < top; ++i)
        {
            int index = indexes[i];
            printf("%.1f%%: %s\n", predictions[index] * 100, names[index]);
        }
        free_image(in_s);
        free_image(in);
        wait_key_cv(10);// cvWaitKey(10);
        gettimeofday2(&tval_after, NULL);
        timersub(&tval_after, &tval_before, &tval_result);
        float curr = 1000000.f / ((long int)tval_result.tv_usec);
        fps = .9 * fps + .1 * curr;
    }
#endif
}


void run_classifier(int argc, char** argv)
{
    if (argc < 4)
    {
        fprintf(stderr, "usage: %s %s [train/test/valid] [cfg] [weights (optional)]\n", argv[0], argv[1]);
        return;
    }
    int mjpeg_port = find_int_arg(argc, argv, "-mjpeg_port", -1);
    char* gpu_list = find_char_arg(argc, argv, "-gpus", 0);
    int* gpus = 0;
    int gpu = 0;
    int ngpus = 0;
    if (gpu_list)
    {
        printf("%s\n", gpu_list);
        int len = strlen(gpu_list);
        ngpus = 1;
        int i;
        for (i = 0; i < len; ++i)
        {
            if (gpu_list[i] == ',')
                ++ngpus;
        }
        gpus = (int*)calloc(ngpus, sizeof(int));
        for (i = 0; i < ngpus; ++i)
        {
            gpus[i] = atoi(gpu_list);
            gpu_list = strchr(gpu_list, ',') + 1;
        }
    }
    else
    {
        gpu = gpu_index;
        gpus = &gpu;
        ngpus = 1;
    }
    int dont_show = find_arg(argc, argv, "-dont_show");
    int calc_topk = find_arg(argc, argv, "-topk");
    int cam_index = find_int_arg(argc, argv, "-c", 0);
    int top = find_int_arg(argc, argv, "-t", 0);
    int clear = find_arg(argc, argv, "-clear");
    char* data = argv[3];
    char* cfg = argv[4];
    char* weights = (argc > 5) ? argv[5] : 0;
    char* filename = (argc > 6) ? argv[6] : 0;
    char* layer_s = (argc > 7) ? argv[7] : 0;
    int layer = layer_s ? atoi(layer_s) : -1;
    if (0 == strcmp(argv[2], "predict"))
        predict_classifier(data, cfg, weights, filename, top);
    else if (0 == strcmp(argv[2], "try"))
        try_classifier(data, cfg, weights, filename, atoi(layer_s));
    else if (0 == strcmp(argv[2], "train"))
        train_classifier(data, cfg, weights, gpus, ngpus, clear, dont_show, mjpeg_port, calc_topk);
    else if (0 == strcmp(argv[2], "demo"))
        demo_classifier(data, cfg, weights, cam_index, filename);
    else if (0 == strcmp(argv[2], "gun"))
        gun_classifier(data, cfg, weights, cam_index, filename);
    else if (0 == strcmp(argv[2], "threat"))
        threat_classifier(data, cfg, weights, cam_index, filename);
    else if (0 == strcmp(argv[2], "test"))
        test_classifier(data, cfg, weights, layer);
    else if (0 == strcmp(argv[2], "label"))
        label_classifier(data, cfg, weights);
    else if (0 == strcmp(argv[2], "valid"))
        validate_classifier_single(data, cfg, weights, NULL, -1);
    else if (0 == strcmp(argv[2], "validmulti"))
        validate_classifier_multi(data, cfg, weights);
    else if (0 == strcmp(argv[2], "valid10"))
        validate_classifier_10(data, cfg, weights);
    else if (0 == strcmp(argv[2], "validcrop"))
        validate_classifier_crop(data, cfg, weights);
    else if (0 == strcmp(argv[2], "validfull"))
        validate_classifier_full(data, cfg, weights);
}
